{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# This is a sample Jupyter Notebook\n",
    "\n",
    "Below is an example of a code cell. \n",
    "Put your cursor into the cell and press Shift+Enter to execute it and select the next one, or click 'Run Cell' button.\n",
    "\n",
    "Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings.\n",
    "\n",
    "To learn more about Jupyter Notebooks in PyCharm, see [help](https://www.jetbrains.com/help/pycharm/ipython-notebook-support.html).\n",
    "For an overview of PyCharm, go to Help -> Learn IDE features or refer to [our documentation](https://www.jetbrains.com/help/pycharm/getting-started.html)."
   ],
   "id": "8a77807f92f26ee"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "#### pip install pandas -i https://pypi.tuna.tsinghua.edu.cn/simple",
   "id": "52cd8694d439a8a7"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "print(\"Hello World!\")\n",
   "id": "fbc121e30a2defb3"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "c4e1bbcaf2cd2601"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-08T03:13:40.637738Z",
     "start_time": "2025-09-08T03:13:40.577565Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "l = np.array([2,3,4,6])\n",
    "print(l)\n",
    "plt.title(\"xxxx\")\n",
    "plt.show()\n",
    "df=pd.DataFrame(data=l,index=['one','two','three','four'])\n",
    "print(df)"
   ],
   "id": "80ae3ec4f581a65e",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 3 4 6]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       0\n",
      "one    2\n",
      "two    3\n",
      "three  4\n",
      "four   6\n"
     ]
    }
   ],
   "execution_count": 4
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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